Genetic algortihms applied to the sensor placement problem and the system identification problem.

L. Yao, W. A. Sethares and D. C. Kammer, "Sensor placement for on- orbit modal identification of large space structures via a genetic algorithm," Journal of the American Institute of Aeronautics and Astronautics, Vol. 31, No. 10, Oct. 1993. [Solving the modal identification problem with the genetic algorithm gives better answers than any of the competing suboptimal methods, at the expense of a larger computational burden.]

L. Yao and W. A. Sethares, "Nonlinear parameter estimation via the genetic algorithm" IEEE Trans. on Signal Processing, Vol. 42, No. 4, April 1994. [The genetic algorithm is modified to attack the problem of identification of parameters in nonlinear systems. The convergence of the modified algorithm is analyzed. This explains why earlier attempts at use of the genetic algorithm in system identification failed.]

A population-based gradient algorithm that can be guaranteed to converge to the global minimum. Estimates of size of optimal population are obtained via a deterministic averaging approach.

K. L. Blackmore, R. C. Williamson, I. M. Y. Mareels, and W. A. Sethares, "Online Learning via Congregational Gradient Descent," Mathematics of Controls, Systems, and Signals, 10:(4) 331-363, 1997.

To get to my homepage, click here.